Add py311, remove py37

.github/workflows/pythonpackage.yml

@@ -11,7 +11,7 @@ jobs:
     runs-on: ubuntu-latest
     strategy:
       matrix:
-        python-version: ['3.7', '3.8', '3.9', '3.10']
+        python-version: ['3.8', '3.9', '3.10', '3.11']
 
     steps:
       - uses: actions/checkout@v2

.pre-commit-config.yaml

@@ -37,7 +37,7 @@ repos:
         types: [file, python]
         language: system
         files: ^ngboost
-        entry: pylint --disable=invalid-name,no-member,missing-docstring,no-self-argument,arguments-differ,R0801
+        entry: pylint --disable=invalid-name,no-member,missing-docstring,no-self-argument,arguments-differ
       - id: pylint-tests
         name: pylint on tests*
         language: system

ngboost/__init__.py

@@ -6,7 +6,9 @@
     # before python 3.8
     from importlib_metadata import version
 
-from .api import NGBClassifier, NGBRegressor, NGBSurvival  # NOQA
-from .ngboost import NGBoost  # NOQA
+from .api import NGBClassifier, NGBRegressor, NGBSurvival
+from .ngboost import NGBoost
+
+__all__ = ["NGBClassifier", "NGBRegressor", "NGBSurvival", "NGBoost"]
 
 __version__ = version(__name__)

ngboost/distns/__init__.py

@@ -1,12 +1,32 @@
 """NGBoost distributions"""
-from .categorical import Bernoulli, k_categorical  # NOQA
-from .cauchy import Cauchy  # NOQA
-from .distn import ClassificationDistn, Distn, RegressionDistn  # NOQA
-from .exponential import Exponential  # NOQA
-from .gamma import Gamma  # NOQA
-from .laplace import Laplace  # NOQA
-from .lognormal import LogNormal  # NOQA
-from .multivariate_normal import MultivariateNormal  # NOQA
-from .normal import Normal, NormalFixedVar  # NOQA
-from .poisson import Poisson  # NOQA
-from .t import T, TFixedDf, TFixedDfFixedVar  # NOQA
+from .categorical import Bernoulli, k_categorical
+from .cauchy import Cauchy
+from .distn import ClassificationDistn, Distn, RegressionDistn
+from .exponential import Exponential
+from .gamma import Gamma
+from .laplace import Laplace
+from .lognormal import LogNormal
+from .multivariate_normal import MultivariateNormal
+from .normal import Normal, NormalFixedVar
+from .poisson import Poisson
+from .t import T, TFixedDf, TFixedDfFixedVar
+
+__all__ = [
+    "Bernoulli",
+    "k_categorical",
+    "Cauchy",
+    "ClassificationDistn",
+    "Distn",
+    "RegressionDistn",
+    "Exponential",
+    "Gamma",
+    "Laplace",
+    "LogNormal",
+    "MultivariateNormal",
+    "Normal",
+    "NormalFixedVar",
+    "Poisson",
+    "T",
+    "TFixedDf",
+    "TFixedDfFixedVar",
+]

ngboost/distns/categorical.py

@@ -1,5 +1,5 @@
 """The NGBoost categorial distribution and scores"""
-# pylint: disable=invalid-unary-operand-type, unused-argument, no-self-use
+# pylint: disable=invalid-unary-operand-type, unused-argument
 import numpy as np
 import scipy as sp
 

ngboost/ngboost.py

@@ -2,7 +2,7 @@
 # pylint: disable=line-too-long,too-many-instance-attributes,too-many-arguments
 # pylint: disable=unused-argument,too-many-locals,too-many-branches,too-many-statements
 # pylint: disable=unused-variable,invalid-unary-operand-type,attribute-defined-outside-init
-# pylint: disable=redundant-keyword-arg,protected-access
+# pylint: disable=redundant-keyword-arg,protected-access,unnecessary-lambda-assignment
 import numpy as np
 from sklearn.base import clone
 from sklearn.model_selection import train_test_split
@@ -362,14 +362,14 @@ def partial_fit(
             best_val_loss = np.inf
 
         if not train_loss_monitor:
-            train_loss_monitor = lambda D, Y, W: D.total_score(  # NOQA
+            train_loss_monitor = lambda D, Y, W: D.total_score(  # noqa: E731
                 Y, sample_weight=W
             )
 
         if not val_loss_monitor:
-            val_loss_monitor = lambda D, Y: D.total_score(  # NOQA
+            val_loss_monitor = lambda D, Y: D.total_score(  # noqa: E731
                 Y, sample_weight=val_sample_weight
-            )  # NOQA
+            )
 
         for itr in range(len(self.col_idxs), self.n_estimators + len(self.col_idxs)):
             _, col_idx, X_batch, Y_batch, weight_batch, P_batch = self.sample(
@@ -386,7 +386,6 @@ def partial_fit(
             proj_grad = self.fit_base(X_batch, grads, weight_batch)
             scale = self.line_search(proj_grad, P_batch, Y_batch, weight_batch)
 
-            # pdb.set_trace()
             params -= (
                 self.learning_rate
                 * scale
@@ -587,8 +586,10 @@ def feature_importances_(self):
 
         if not all_params_importances:
             return np.zeros(
-                len(self.base_models[0]),
-                self.base_models[0][0].n_features_,
+                (
+                    len(self.base_models[0]),
+                    self.base_models[0][0].n_features_,
+                ),
                 dtype=np.float64,
             )
 

pyproject.toml

@@ -13,7 +13,7 @@ classifiers = [
 license = "Apache License 2.0"
 
 [tool.poetry.dependencies]
-python = ">=3.7.1, <3.11"
+python = ">=3.8, <3.12"
 scikit-learn = ">=1.0.2"
 numpy = ">=1.21.2"
 scipy = ">=1.7.2"

tests/conftest.py

@@ -5,8 +5,8 @@
 from sklearn.datasets import fetch_california_housing, load_breast_cancer
 from sklearn.model_selection import train_test_split
 
-Tuple4Array = Tuple[np.array, np.array, np.array, np.array]
-Tuple5Array = Tuple[np.array, np.array, np.array, np.array, np.array]
+Tuple4Array = Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]
+Tuple5Array = Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]
 
 
 def pytest_addoption(parser):
@@ -22,6 +22,11 @@ def pytest_configure(config):
     config.addinivalue_line("markers", "slow: ")
 
 
+@pytest.fixture(scope="session", autouse=True)
+def set_seed():
+    np.random.seed(0)
+
+
 @pytest.fixture(scope="session")
 def california_housing_data() -> Tuple4Array:
     X, Y = fetch_california_housing(return_X_y=True)

tests/test_distns.py

@@ -52,6 +52,12 @@ def classification_data():
     return X_train, X_test, y_train, y_test
 
 
+def is_t_distribution(
+    dist, learner, regression_data
+):  # pylint: disable=unused-argument
+    return dist == T
+
+
 @pytest.mark.slow
 @pytest.mark.parametrize(
     "dist",
@@ -64,6 +70,9 @@ def classification_data():
         DecisionTreeRegressor(criterion="friedman_mse", max_depth=5),
     ],
 )
+@pytest.mark.xfail(
+    condition=is_t_distribution, reason="Known to fail with T distribution"
+)
 def test_dists_runs_on_examples_logscore(dist: Distn, learner, regression_data):
     X_train, X_test, y_train, y_test = regression_data
     # TODO: test early stopping features

tests/test_score.py

@@ -1,3 +1,4 @@
+# pylint: disable=unnecessary-lambda-assignment
 from typing import List, Tuple
 
 import numpy as np